OpenAI proves ω ≤ 9/4 for matrix multiplication
Result confirmed
Importance: historic (5 of 5)The takeaway
Family 107 of OpenAI’s 6 Oct 2026 release proves that two n × n matrices can be multiplied with O(n^(9/4+ε)) arithmetic operations, the largest drop in the exponent in decades; the best bound before was ω < 2.371177, set in August 2026 with AlphaEvolve. The 13-page proof is non-constructive and comes with a Lean formalization. By 21 Oct, Amanov, van den Berg and Zuiddam had re-proved it over every field and Rafael Pass had given an explicit construction. The resulting algorithms are galactic and give no practical speedup.
Status
- Claim
Result confirmed
- Our reporting
- Medium confidence
- Verification
- Lean-verified
- Importance
- Historic (5 of 5)
- Last verified
- 29 October 2026
Your AI and this story
- GPT-6 Astra159 days after its cutoff
- Claude Opus 5.598 days after its cutoff
- Gemini 4 Argon189 days after its cutoff
- Grok 4.7128 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 98 days before it.
Key facts
- Theorem 1.1 (verbatim): ‘For every ε > 0, two n × n complex matrices can be multiplied using Oε(n^{9/4+ε}) arithmetic operations. In particular, ω ≤ 9/4.’ The paper adds that its proof ‘does not specify a competitive finite matrix size’. 13 pages, dated 2 Oct 2026
- Companions in the family: ‘Complex Matrix Multiplication Below 2.258 and Rectangular Bounds’ (83 pp., 24 Sep; ω < 2.258, dual exponent α > 0.465, ω(1, 0.709, 1) < 2.092 in characteristic zero) and ‘Staggered extraction for exact matrix multiplication over every field’ (35 pp., 24 Sep; ω < 2.371054886006746 over every fixed field, including positive characteristic)
- Method (paper): ‘The present proof combines explicit tensor degenerations with the spectral viewpoint.’ A separation construction turns blocks that share one tensor leg into independent blocks; two inequalities for polynomial-multiplication tensors force a growth rate a^{4/3}, which gives 3t ≤ 9/4 for every spectral character
- Previous records (as listed by Pass): ω < 2.371339 (Alman et al., 2025) and ω < 2.371177 (Dupont et al., August 2026, found with AlphaEvolve; see AlphaEvolve helps lower the matrix multiplication exponent ω to below 2.371177)
- Lean: comparator challenge MatrixMultiplication.lean defines straight-line programs with constant, input, add, subtract and multiply gates, counts the arithmetic gates, and defines ω as the infimum of exponents τ admitting correct programs of cost C·n^(τ+ε) for every n; theorem complex_omega_le_nine_quarters (plus α > 93/200 and the rectangular bound). The oai-math-revised comparator run (25–26 Oct) accepted it
- Amanov, van den Berg and Zuiddam (arXiv 2610.30054, 21 Oct, verbatim): ‘We give an exposition of OpenAI’s recent proof that the exponent ωC of matrix multiplication over the complex numbers satisfies ωC ≤ 9/4. Our version of the proof gives ωF ≤ 9/4 over every field F.’ They prove an ‘operational barrier’ showing the current analysis cannot be pushed further with the same ingredients, and note that Sela Navot, Kunal Marwaha and they themselves each released Lean proofs of ω_F ≤ 9/4 for every field on 7 Oct
- Rafael Pass (arXiv 2610.28627, 20 Oct, verbatim): ‘We give a self-contained constructive proof that the matrix multiplication exponent satisfies ω ≤ 9/4 … We explicitly construct a Strassen-style matrix multiplication identity and obtain the bound by ordinary block recursion.’ His AI statement says Codex (Astra) was used extensively, while ‘The main ideas behind the construction, analysis, and abstractions are my own’
- Other follow-ups: Przemysław Uznański (arXiv 2610.10494) extends the analysis to rectangular products, ω(1, 1/2, 1) = 2, and all-pairs shortest paths in directed unweighted graphs in O(n^2.5); Houxi Lü (arXiv 2610.18914) replaces a Fourier step by polynomial interpolation to get ω ≤ 9/4 in every characteristic
Show 2 more
- Reactions: Steven Strogatz: ‘This leap in progress is like Bob Beamon’s long jump’; Abbas Mehrabian (X, 8 Oct): ‘a groundbreaking algorithm to multiply matrices, showing ω⩽2.25. Our team had the best bound before OpenAI’s result’; Virginia Vassilevska Williams (Scientific American): ‘In a maybe diabolical sense, the advance on matrix multiplication can be seen as an attempt by the LLMs to speed themselves up’; Yuval Filmus (X, 17 Oct): the proof is ‘outlandish, using Strassen’s theory of asymptotic spectra’
- Practicality: Sergey Gusev (X, 20 Oct) says the explicit algorithms built so far are ‘galactic’; a first AI-written construction needed matrices with about 10^(2.3×10^11) rows even at exponent 2.67, later reduced to about 10^4,650 rows
What happened
The exponent ω measures how fast matrices can be multiplied: the schoolbook method costs n³ operations, Strassen showed in 1969 that fewer suffice, and a long line of tensor-based “laser method” refinements brought the bound to about 2.371 by 2025–2026, each step gaining a few thousandths. OpenAI’s 6 Oct release includes a 13-page manuscript, dated 2 Oct, that proves ω ≤ 9/4 = 2.25 over the complex numbers. Two companion papers from late September give the intermediate bound 2.258, rectangular bounds, and a small improvement over every field.
The proof does not exhibit an algorithm. It works in Strassen’s spectral framework: it bounds every “character” of the matrix multiplication tensor by comparing it with polynomial multiplication, using a new way of separating tensor blocks that share one leg. OpenAI formalized the main bound in Lean, with a statement that defines arithmetic programs and their cost from scratch; the community comparator run accepted it.
Why it matters
ω is one of the central constants of algebraic complexity theory, and a jump from 2.371 to 2.25 is far larger than anything achieved since the 1980s. Practical matrix multiplication is unaffected: the constructions behave like “galactic” algorithms whose advantage appears only at sizes far beyond any computer.
Independent checks
Within three weeks the result had been re-derived by others. Alimzhan Amanov, Maxim van den Berg and Jeroen Zuiddam wrote an exposition in the language of degenerations and asymptotic spectra, extended it to every field and to rectangular shapes, and showed that this analysis cannot give a better exponent with the same ingredients. Rafael Pass gave an explicit Strassen-style identity that reaches 9/4 by ordinary recursion. Both papers used AI assistance. Several people also released Lean proofs for arbitrary fields.
Sources
13 sources from 4 sites. Numbers match the chips in the text.
13 sources: 9 primary, 1 press, 3 reactions
Primary
- OpenAI: An Upper Bound of 9/4 for the Matrix Multiplication Exponent (2 Oct)github.com, paper
- OpenAI: Complex Matrix Multiplication Below 2.258 and Rectangular Boundsgithub.com, paper
- Lean scope note for family 107github.com, code
- Lean comparator challenge: MatrixMultiplication.leangithub.com, code
- arXiv 2610.30054: Amanov, van den Berg, Zuiddam, Multiplying matrices via polynomials: exposition and barriersarxiv.org, paper
- arXiv 2610.28627: Rafael Pass, A Constructive and Elementary Proof of ω ≤ 9/4arxiv.org, paper
- arXiv 2610.10494: Przemysław Uznański, Rectangular matrix multiplication from polynomial productsarxiv.org, paper
- arXiv 2610.18914: Houxi Lü, matrix multiplication in arbitrary characteristicarxiv.org, paper
- oai-math-revised: comparator run on all challenges (LEAN_CHECKS.md)github.com, code
Press
- Scientific American: The most exciting claims from OpenAI’s heap of new proofs (Vassilevska Williams on 107)scientificamerican.com, press
Reactions
- Abbas Mehrabian on X (8 Oct)x.com, discussion
- Yuval Filmus on X (17 Oct)x.com, discussion
- Sergey Gusev on X: explicit 9/4-type algorithms are galactic (20 Oct)x.com, discussion
Changes
- Filed (families 001–125; manuscript, Lean statement, comparator results and the follow-up arXiv papers read)